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To Diff or Not to Diff? Structure-Aware and Adaptive Output Formats for Efficient LLM-based Code Editing
Wei Cheng, Yongchang Cao, Chen Shen +4
Large Language Models (LLMs) are increasingly used for code editing, yet the prevalent full-code generation paradigm suffers from severe efficiency bottlenecks, posing challenges f…
Self-Improving Code Generation via Semantic Entropy and Behavioral Consensus
Huan Zhang, Wei Cheng, Wei Hu
Improving the code generation capabilities of large language models (LLMs) typically relies on supervised fine-tuning or preference optimization, both of which require costly exter…
Bootstrapping Code Translation with Weighted Multilanguage Exploration
Yuhan Wu, Huan Zhang, Wei Cheng +3
Code translation across multiple programming languages is essential yet challenging due to two vital obstacles: scarcity of parallel data paired with executable test oracles, and o…
A Pair Programming Framework for Code Generation via Multi-Plan Exploration and Feedback-Driven Refinement
Huan Zhang, Wei Cheng, Yuhan Wu +1
Large language models (LLMs) have achieved impressive performance on code generation. Although prior studies enhanced LLMs with prompting techniques and code refinement, they still…
Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion
Wei Cheng, Yuhan Wu, Wei Hu
Recent years have witnessed the deployment of code language models (LMs) in various code intelligence tasks such as code completion. Yet, it is challenging for pre-trained LMs to g…